{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UCI Metro dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from pathlib import Path\n",
    "from config import data_raw_folder, data_processed_folder\n",
    "from timeeval import Datasets\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for source datasets in /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Metro and\n",
      "saving processed datasets in /home/projects/akita/data/benchmark-data/data-processed\n"
     ]
    }
   ],
   "source": [
    "dataset_collection_name = \"Metro\"\n",
    "source_folder = Path(data_raw_folder) / \"UCI ML Repository/Metro\"\n",
    "target_folder = Path(data_processed_folder)\n",
    "\n",
    "from pathlib import Path\n",
    "print(f\"Looking for source datasets in {source_folder.absolute()} and\\nsaving processed datasets in {target_folder.absolute()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset transformation and pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directories /home/projects/akita/data/benchmark-data/data-processed/multivariate/Metro already exist\n"
     ]
    }
   ],
   "source": [
    "train_type = \"unsupervised\"\n",
    "train_is_normal = False\n",
    "input_type = \"multivariate\"\n",
    "datetime_index = True\n",
    "dataset_type = \"real\"\n",
    "\n",
    "# create target directory\n",
    "dataset_subfolder = os.path.join(input_type, dataset_collection_name)\n",
    "target_subfolder = os.path.join(target_folder, dataset_subfolder)\n",
    "try:\n",
    "    os.makedirs(target_subfolder)\n",
    "    print(f\"Created directories {target_subfolder}\")\n",
    "except FileExistsError:\n",
    "    print(f\"Directories {target_subfolder} already exist\")\n",
    "    pass\n",
    "\n",
    "dm = Datasets(target_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Metro/Metro_Interstate_Traffic_Volume.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/Metro/metro-traffic-volume.test.csv\n"
     ]
    }
   ],
   "source": [
    "# get target filenames\n",
    "dataset_name = \"metro-traffic-volume\"\n",
    "filename = f\"{dataset_name}.test.csv\"\n",
    "\n",
    "source_file = source_folder / \"Metro_Interstate_Traffic_Volume.csv\"\n",
    "path = os.path.join(dataset_subfolder, filename)\n",
    "target_filepath = os.path.join(target_subfolder, filename)\n",
    "\n",
    "# transform file\n",
    "df = pd.read_csv(source_file)\n",
    "df = df[[\"date_time\", \"traffic_volume\", \"temp\", \"rain_1h\", \"snow_1h\", \"clouds_all\", \"holiday\"]].copy()\n",
    "df.insert(0, \"timestamp\", pd.to_datetime(df[\"date_time\"]))\n",
    "df.loc[df[\"holiday\"] == \"None\", \"is_anomaly\"] = 0\n",
    "df.loc[~(df[\"holiday\"] == \"None\"), \"is_anomaly\"] = 1\n",
    "df[\"is_anomaly\"] = df[\"is_anomaly\"].astype(int)\n",
    "df = df.drop(columns=[\"date_time\", \"holiday\"])\n",
    "df.to_csv(target_filepath, index=False)\n",
    "print(f\"Processed source dataset {source_file} -> {target_filepath}\")\n",
    "\n",
    "dataset_length = len(df)\n",
    "\n",
    "# save metadata\n",
    "dm.add_dataset((dataset_collection_name, dataset_name),\n",
    "    train_path = None,\n",
    "    test_path = path,\n",
    "    dataset_type = dataset_type,\n",
    "    datetime_index = datetime_index,\n",
    "    split_at = None,\n",
    "    train_type = train_type,\n",
    "    train_is_normal = train_is_normal,\n",
    "    input_type = input_type,\n",
    "    dataset_length = dataset_length\n",
    ")\n",
    "\n",
    "dm.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>train_path</th>\n",
       "      <th>test_path</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>datetime_index</th>\n",
       "      <th>split_at</th>\n",
       "      <th>train_type</th>\n",
       "      <th>train_is_normal</th>\n",
       "      <th>input_type</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection_name</th>\n",
       "      <th>dataset_name</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Metro</th>\n",
       "      <th>metro-traffic-volume</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/Metro/metro-traffic-volume.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>48204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     train_path  \\\n",
       "collection_name dataset_name                      \n",
       "Metro           metro-traffic-volume        NaN   \n",
       "\n",
       "                                                                             test_path  \\\n",
       "collection_name dataset_name                                                             \n",
       "Metro           metro-traffic-volume  multivariate/Metro/metro-traffic-volume.test.csv   \n",
       "\n",
       "                                     dataset_type  datetime_index  split_at  \\\n",
       "collection_name dataset_name                                                  \n",
       "Metro           metro-traffic-volume         real            True       NaN   \n",
       "\n",
       "                                        train_type  train_is_normal  \\\n",
       "collection_name dataset_name                                          \n",
       "Metro           metro-traffic-volume  unsupervised            False   \n",
       "\n",
       "                                        input_type  length  \n",
       "collection_name dataset_name                                \n",
       "Metro           metro-traffic-volume  multivariate   48204  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.refresh()\n",
    "dm._df.loc[slice(dataset_collection_name, dataset_collection_name)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experimentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>rain_1h</th>\n",
       "      <th>snow_1h</th>\n",
       "      <th>clouds_all</th>\n",
       "      <th>weather_main</th>\n",
       "      <th>weather_description</th>\n",
       "      <th>date_time</th>\n",
       "      <th>traffic_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>288.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>2012-10-02 09:00:00</td>\n",
       "      <td>5545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>289.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2012-10-02 10:00:00</td>\n",
       "      <td>4516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>289.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2012-10-02 11:00:00</td>\n",
       "      <td>4767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>290.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2012-10-02 12:00:00</td>\n",
       "      <td>5026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>291.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2012-10-02 13:00:00</td>\n",
       "      <td>4918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>48199</th>\n",
       "      <td>None</td>\n",
       "      <td>283.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2018-09-30 19:00:00</td>\n",
       "      <td>3543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48200</th>\n",
       "      <td>None</td>\n",
       "      <td>282.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 20:00:00</td>\n",
       "      <td>2781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48201</th>\n",
       "      <td>None</td>\n",
       "      <td>282.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Thunderstorm</td>\n",
       "      <td>proximity thunderstorm</td>\n",
       "      <td>2018-09-30 21:00:00</td>\n",
       "      <td>2159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48202</th>\n",
       "      <td>None</td>\n",
       "      <td>282.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 22:00:00</td>\n",
       "      <td>1450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48203</th>\n",
       "      <td>None</td>\n",
       "      <td>282.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 23:00:00</td>\n",
       "      <td>954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48204 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      holiday    temp  rain_1h  snow_1h  clouds_all  weather_main  \\\n",
       "0        None  288.28      0.0      0.0          40        Clouds   \n",
       "1        None  289.36      0.0      0.0          75        Clouds   \n",
       "2        None  289.58      0.0      0.0          90        Clouds   \n",
       "3        None  290.13      0.0      0.0          90        Clouds   \n",
       "4        None  291.14      0.0      0.0          75        Clouds   \n",
       "...       ...     ...      ...      ...         ...           ...   \n",
       "48199    None  283.45      0.0      0.0          75        Clouds   \n",
       "48200    None  282.76      0.0      0.0          90        Clouds   \n",
       "48201    None  282.73      0.0      0.0          90  Thunderstorm   \n",
       "48202    None  282.09      0.0      0.0          90        Clouds   \n",
       "48203    None  282.12      0.0      0.0          90        Clouds   \n",
       "\n",
       "          weather_description            date_time  traffic_volume  \n",
       "0            scattered clouds  2012-10-02 09:00:00            5545  \n",
       "1               broken clouds  2012-10-02 10:00:00            4516  \n",
       "2             overcast clouds  2012-10-02 11:00:00            4767  \n",
       "3             overcast clouds  2012-10-02 12:00:00            5026  \n",
       "4               broken clouds  2012-10-02 13:00:00            4918  \n",
       "...                       ...                  ...             ...  \n",
       "48199           broken clouds  2018-09-30 19:00:00            3543  \n",
       "48200         overcast clouds  2018-09-30 20:00:00            2781  \n",
       "48201  proximity thunderstorm  2018-09-30 21:00:00            2159  \n",
       "48202         overcast clouds  2018-09-30 22:00:00            1450  \n",
       "48203         overcast clouds  2018-09-30 23:00:00             954  \n",
       "\n",
       "[48204 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_file = source_folder / \"Metro_Interstate_Traffic_Volume.csv\"\n",
    "df = pd.read_csv(source_file)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48201</th>\n",
       "      <td>2018-09-30 21:00:00</td>\n",
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       "      <td>282.73</td>\n",
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       "      <td>282.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48203</th>\n",
       "      <td>2018-09-30 23:00:00</td>\n",
       "      <td>954</td>\n",
       "      <td>282.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48204 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp  traffic_volume    temp  rain_1h  snow_1h  \\\n",
       "0     2012-10-02 09:00:00            5545  288.28      0.0      0.0   \n",
       "1     2012-10-02 10:00:00            4516  289.36      0.0      0.0   \n",
       "2     2012-10-02 11:00:00            4767  289.58      0.0      0.0   \n",
       "3     2012-10-02 12:00:00            5026  290.13      0.0      0.0   \n",
       "4     2012-10-02 13:00:00            4918  291.14      0.0      0.0   \n",
       "...                   ...             ...     ...      ...      ...   \n",
       "48199 2018-09-30 19:00:00            3543  283.45      0.0      0.0   \n",
       "48200 2018-09-30 20:00:00            2781  282.76      0.0      0.0   \n",
       "48201 2018-09-30 21:00:00            2159  282.73      0.0      0.0   \n",
       "48202 2018-09-30 22:00:00            1450  282.09      0.0      0.0   \n",
       "48203 2018-09-30 23:00:00             954  282.12      0.0      0.0   \n",
       "\n",
       "       clouds_all  is_anomaly  \n",
       "0              40           0  \n",
       "1              75           0  \n",
       "2              90           0  \n",
       "3              90           0  \n",
       "4              75           0  \n",
       "...           ...         ...  \n",
       "48199          75           0  \n",
       "48200          90           0  \n",
       "48201          90           0  \n",
       "48202          90           0  \n",
       "48203          90           0  \n",
       "\n",
       "[48204 rows x 7 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df[[\"date_time\", \"traffic_volume\", \"temp\", \"rain_1h\", \"snow_1h\", \"clouds_all\", \"holiday\"]].copy()\n",
    "df1.insert(0, \"timestamp\", pd.to_datetime(df1[\"date_time\"]))\n",
    "df1.loc[df1[\"holiday\"] == \"None\", \"is_anomaly\"] = 0\n",
    "df1.loc[~(df1[\"holiday\"] == \"None\"), \"is_anomaly\"] = 1\n",
    "df1[\"is_anomaly\"] = df1[\"is_anomaly\"].astype(int)\n",
    "df1 = df1.drop(columns=[\"date_time\", \"holiday\"])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V5a46/en9maqqbt5raVxoNr7PZqYWPO/yj+SHeDfm/ona+xbIoge/XKDOvQYm1bbItB4PD9f5T4zyuhh5h5xjcGJc6MZM4xDgWHvN5BWbVVVdr1dGMzEO/IEAFVry6Ly3YXuN3py4qumCOdLM1dv0zPClenF082F91lp9NL286XFjN9SBc9Yx20QGeNGtO1b3ZaTnmeFLm4ZXzF1TqaeHLfVkJkxAkrZFGSYXOaxEkl4cvUyfzVqrtyeVZbxMZzwyXK+MXp7x9QDpei2Um8ir3tmL11fp31/MT9gTkdBK+qZlKQ1IMuatqdS7kzNfJ+eD6rqGrMyYvnhDVdTn023GEyBFphGgynHTV21Vj4eGaXt1eoEYa/2frqQx8FRb37xiHDR3vf7+0Zymx1bBXF63vztDf3q/8C62L39urH7bb3rG15PNXl2vjeWuTqY1NjiqEwQDb+gzRS+nccF+5iPDdW1vJhLwm5r6Bs8anZE5MlI9F30yozzq8409B2N5b2qZxi+LPgRwXWW1Hhm8KLUCAQVg044aXfb0GF369Bi9Mb60qbdjJJ83MXNKeE09cfnmph4yu2sbtNujmaUvf26c/vnpXE/WnU0Tl2/W2KXp5U375ydzdW3vSVq12Z3hr5/MKFd9xPn7ya9apkBJZMj89Y5mBEx0TgXSRYDKLyKuC5xe/D/19RJVVNVo9uptWr1llzbtCJ6kPp1R3nKhDr0yJreCAZG9pKzdkzgw2t33pLkYubs7wcl78orNLYa5xFNbH2hxUTlvzXYNmZ/5WRmzeSm7eYfzGYriJZpEULIN2PDE1GOWbNSjgxc1Pffx9PK4+ciGL9ygC/83qimX0NrKak1akd7d37qGgG57e7oWr49+dxDJ++49Q/SrN6d6su6rXpqgHTXRk8A6ZSTNLNvW9Hhj2EXyWxPjz0o2s2ybfvHa5GbPba+u0/pKhrvCX5ZsqNJpDw5TQ6D5jQQ3cm+m6vNZa7UorC6O1mJKp3zWWlXHmLU534xbukn9p65O6jM/f3VS002fbv8eqmP+NSStMkwt3aKVm3Y2nbMLra/M4vVVemdy7HPGz1+dpOtfjz7Sw6nG4yVW8vNkLd+4Uw8OXLjnccUOPTui5SRSjayVlobqkoqq5ue5ZCdJ+MN7M/WDZ8YmV2AgAQJUeeScx0Y2NfKfHbFMZVt2eVyi+JZV7GjKIXL965P1yz4OK/yIhk5RRGto4brtTTMeudJmc7Hh906C7s8/6z1JFySRr+GoewbHHBLptvlrg3dMYsXrNu+o0c40LzJjmRxjZsQ+41tOt/zs8Ngn5ULXuO2+WrAhqc+NiXK3sLEx9NcPZ+uO92c1e+2nr+zpIdXrk7lasXFnwtxx8QxfuEFvTSxtejxvTaUGz1uvOz+eE/tDPlVd16DNO6L3MPDaSA9n06qrT3DBnWQ1HGtabqcueXKMej4yPK1lAG7rM26lNu2o0a6a3ArYTFy+OeYhHH6ob91Zq2eHL202Y9g7k8t09L1DVL7V321aN/zf65NTOq8t2RBMyO5GLrxrXp6oC54Ypd9E9MT3+ygLt1z69Bjd/Wl2ZrKUFLP3rhMvjYremz1WL6jGTbiucrf6jC/Vph01+jqiPTh7dcveUY8MWqhA2IEafgPoi9lrtYAJiuAyAlR5Ilo0v9ZHeXvenVymzr0GNrsw+96To/V/oXKPXbop5ljpRCJPmv/5ckFT0CpgrXbV1qtzr4FNvTweG7JIvcfEHqL03yH+Hc7xtw9nq3Ovgc2eizs7lYv+8G7z4ZKRF5CPDl5UMHc5C00gSlUyYlFFzPdPiRFQdMqGLmU+DOWWu+XNafrXgPlpLdMv+k1cpVMfTC94ku9q6wN6d0r0YL4J659hM3hvf32MyQLqGgK6+9O5qoh43cseLEA29R6zvNlM05F7frQjoSasF66J+D/cPz+dqye/XqLxy/e0axpnnV21OX8CVHUNAT34pb9nxYt3js8XSzdUtehB5Ibf9puuuz6KHWgMD+jc/3n22jbDQ9v03iTbU6+MWaHwwRq/esubWUFTtboAgtv5hgBVAbnj/Zm68oXxUV/LdNP6/anBi43yrc2H3M1OZba8iIhUtKTdjU9ZBXOISNIzw4JDv14ctVwPD4odhHpp1HJ9MXtt8uWKYlmFO0OQGi/EwpPBe8XEyCQRvg+RQNGftseZNCDZIVZub+OyzbtUGSVJNvLDsooqvTgq2LtxzbbdzS5wI/21/+xmd7DLIi5Mo9VBN4T1wM1k7TNiUYXemVymewdk7w470Oj9JIZ/OckTM2DWmqb8RfFMX7XnpsPDgxbp569OivneZ4Yv1cn/+cpZISXtqm1oyqO6KzT8PDKfjqQWw3C9MmDWmrjBBycGzlnXlNQ+Vz0+1L83c526+Kkx6vGQ+z1lh8xfrw+mRT9Ww4+lfDSjbGtq13YZ9PPewfqK+0i5gwCVzzTOYrSz1v2hUgNmuRN0kaSv5q/X5CzMQOFEUdR+x2ERKrX4M6Htu/f8/vHGcYeLdoH/vSfHtHjOD0GmdDT+3PG6kie68/bH92Zq+MLow8y27Kz1Ve+/fBJ+1yzyN27shbltV/BiJVGiTKf53Zw2CM59fKQuenK0szeHhOfGQnN/+3C21oW2UXWd97/T1S9P1GNDFqu6rkEXPD4q7gVu5L715/6zmv52q9fUio07kv7Mjpr6pmEvkfu1F7ObAvEkGjq0vrJad7w/S7/p17I3xKaIYciPJjFRwLuTy7Q18mZDnMO2z/iV6na/84CW1+54f1bM4INTkQG4yLyQ4effEYs2eNpDM9VV76jJnRtO4+KMRLjns7nNevSla7tLeafc5OZM2T95cYKueGF8s7QMfhFZr8G/CFD5zOzybZKk3g4TlcdrEyd7Tglf1IyyrTHfJ0m39puun/WOfYGRjoQnw4g3RA1PhcWnYv1ENfUNemTQQvWfutqV3iBOc+z87cPZTX8v3VDl6HORyaC/XrBBfwm7aEukpr6hqQeZFJwRsbquIakeXo2/0Ouhu35PxJkhpC5B4ODz2Wt1y5vRuwif8sDXujVKgxnuuvy56Ektt4QS0v8pIq9U2hxcvzttPDQGA+avzf28B5t31DRLxn3OYyP0xNDkZ9+J9NH08qYJJK58MXrP2WwKvwCrjVI/zAqd+6LZWVMfc/+JnAHQqWQuuBuNdDjkJd0EukCjiqpq3TdgXsJgfCoB0qaJK7a1HOLkRjzElUlq8szi9VVRZ1eLDLx/9549ic5v7jtN/SMCYvUJck2F92Cpqq7T8yOWNmvn3vPZXO0K3QivawjotbErErbbqusaHM8YPmv1Ng2cs87Re/0g1pBuSXp7UpleHhW8Jks1l+bPXpno2wl8dtc26IEUh5vGa7O9MNJ/+WAjA3G7auubciHDXwhQeczLm67LKmLfQX45RuK9VM0pD3Y1T6fNE3Pq4ii/YeNT4XedrLXNeo28O7lMr4xZoTs/nqM3J5Q2+7yTaVYjbUtheNLFT43RZc8Ee1nFCxZFJqH+9VvT9MmMPTOnjVpcEfcOW59xpXpq2JKmx93+/ZWOvneIvvfkmJTvKEQG9dzclUelk6yZjgyONCZVjdS4VYcv9Cb/xKYdNZqc5ix/ueTUB4c1JeP+bOYard6yW8/7sGHnlrkxhh39N1HAKEb1tsujKdXj2ZCBnCbIX//32mSdcP/QqK/d+9k8vTlxVUr5gMIPmcYAVsX26qRncU1VrJm9dhVwrsqrX5qgZ0csawoOObUuYkbR/3s9/nDHK8LSeTw8aKGe+GqJvgqb3fntSWVNs5v2m7hKDw5cqD4xhhw2trFHLt7ouKdbaRKzUYcr27zL04DBrxPkVkp1ttnJK7foia+WJH5jVgVriAcHLtTmFANvp0Xk04yWz2vJhmBQ1o95Gns8NFwn5FDvzUJCgKqAfS9sOM0/PpnrYUmc+d07M4J/tMhB1fK9jY0xq+Z3FsPvYITfgdoYEaSZXZ44d0Ok50dGvzvSmOAzlg3bg+t+YWT8oGBlnPxBN74xVQPjrGd3RIMw/CSbqZn3kJt21NR7OsTyf18t0Scz9wRfIy+mpq/amjd3vCKDw425+nJVvGB3YzVcsT36exatd9abs1mSdP+1d4GkjVu2KeZ0805HMSe6+Gt8vcfDw3Xda8He7+nMHiapWa/saGK1WRoCVrNWb9P/vk79gj3dc8DAOeuympy6UePQPrfuo41cnDhwuSM042Nkz9VJoTQdje3BVIMvbjr38ZG65uWJid+YIZEz2mVCPt9DjZbP65qXJ+rZEct8sX9F8mOZEESAKgfE680Tdwa3Amm8R8tB1XiBHd5ms5I27kh9qvtEYg01+TCFXAWNeYDCxZt5UFKzYUJSsNv2E0MXu3YxH60B3BCwGjJvnS/vjCB189cmH6CNJTxo8eUcJ3nwmu9L/wnrel5T36CrXprQbHjoa2OdDYfOtt0O7pCPWZJGT0EPVe6uazHUY8LyTTrtwWEaGnaXPlMS9Tx+d7L7gb7IY8KtPJHW2qz1aEH+iDzjRuthvmpz9F4sM8u2SZJ6RdyYjJeXbWppy/ZNY69sJ8ODIoewxZqwJ5pox3uvj9O7qXr7uzPUN6LnfCaEz7r85oTSFjcLG8XL6RnPTW9Mdfzez8Ju/EjRe6oviDJs3knzLlEdNmDWGnXuNdBRe9TpzQonspUbcFlFlTr3GqjlEcdQovOhMYkv1fzavt4QZ1hkuMbhybmcp/HLOWt1c1/nxxrSR4DKb6LUQ2+ML01rkSbKMhPVd9FmUPHCr9+apr98MCvqa/GqugcHBhtMZVt2Ze1uxSYXg19uTEP/6Yw1en7kMlfy2cTSZ9xK/fbtGfq8xayHzvafx4cu0h/fm+l+weAqJ+2KyJnWWixDptnQvcpddRoyL9jrL14QviKsEdQ4rDS8h9djQzK3f6ciEKpcn4sxuYLXTbQlG9Jv/J/476+aDfXo3Gugrns1OOTkN/2ma0KaPTPiSZQnxalk28o/fHZcs8fjlzWfJCT8IiKZRb8yZoWO+deQ6MEEn16YIHMqd9WlNAPW4HktL4SHzt/TGyTexWHjvnvh//b0qu8/zZ3cnOHcrPuyVY9aa/X2pFVRAzBOZkkMd1+cHlup3Mh0Yl3l7qZZqUfGSJ0Qfk64trez3kvrKpvnF/vVW/Eu3q1eCqUN6fnwcE0rzb8h/I2TUEXm3Vq9pXm76OlhS3RjWFDxbx/O0a4EvXi+TCGXVyqjQBo5PTe+lGYqGE9Obymu9PfvzkxpmDVSR4AqB3hxd9XNA7E67I6RkzsBFVU1TV3Dv16wodlwn2ii9aCKPCkE151w1b7hRsOwMWeBW7NzRGvgNq5jY1VNs5Na+Gxx8bwwcnlTcGv4wg2uDC3zOgCQz+IlCD338ZHNHicaPvLrftP027dnaF3l7rRmtoyWcNtLjfXMqs27tGDtdr08Or1G3Ffz1ycM/iXj9bF78ozsrm3Q0fcObpabxA13xLipkKrwO+rTUkyKHsmt80G6N4Ubk7VH3nlHYfrF65Oa5Q9KVaIcR+E9O6IdCo8PXaw3xjfPSdTyRlT+G7awQvd8Nk//HdIyR97lz41LOddS5G9e12DVb9KqmO+flOKs2U7Sd6QSAPlzRB0fHrCPtu813vTeWdvgOKVI5e46x710csXTEcNiF67brrWV8b/j2ojJBtybQZ3WcjyVu+pkrWUYoEcIUHms8aJ/RyaCUFHqHhunQnLa0I7MWZSo2+YjgxY6W3CYP70fu1dNZHLnROV28r0yGbxyq1trrBwVjsrg4D3z1lSqMk6i90TBxd1pTGU/ZeUW3fLmND0WpRGY7F3KXOfm8Lq0hO00jZv+5Ae+dvzxX7wWP4nrlJXBu6g1dQHFi8cGcimyHOEHz45tCkBEOwadfLVb+03XxU+NTvzGMPFmzyoKO+uv3rpL1XUBPZbBXpb5bMHa7frzB3tmZU22rt8SI+Cby0MhkJ55axzOTJpgFwkPREfjZGh0ZOJkX/R2TuHYmFG2VZ17DdT0Vc567oxZslGdew3UovXbm9q7W6OkXZCkLTGeD1e5u05H3TM44fvu/WxezNcmpTBxyIyyrUlNOJNMGzPekMQXm/Kp7tlW8SZliqa2PqCzHx2h0x9umdMoXY8NWaQ7P5qd+I1KHOhtFO9c7nae11RmUJ+3plKvOLxZlqkmV2PPuVTOb/PWVOqalyc06/CQHOfrXLttt7o/NEwn/ucrvTx6hX703LjEH4LrCFDlMK8SBZ/13xFJvT9y9hEnyrc2v8CKF6QwUSqeaBVgZA4Ev0kUAGqccSWWBwcu1ITle3qtJPt9L39unH7msHt3NF/Eubu6rnK3+o5fGfNuY+OF2mtRZpG5PImTw7CFmU9w6aYN26vV9e5Bmh92URI5lCjc7NXbNGBW/B6FgYDVgFlrmnrhbayqSTrXkVsXyJE94qLtk1ZSUZRgWKPJK/c0zKMd617ZWVOv8x8fqRllzXv0NJa/IZB8wDZWFZBsL8jhcY6D8G3b+Lunk+MiVm/P54YvbTY820/bLlWRh8UPnh0b9lrs77d1Z21T/pe3J63ShGWbVFPf4PoQKuS3ZG5eJNq1UmmXpcNpFZOJI6Lx/Dd6ibOhx429y6Ll3YpmwvJN6txrYMzePvPWVLY4F0bWFpmISV+f4CaR1DyPUKycXNHO2+E5VyODBpGz0Mab4CeWdZW7VZWhnisvjlqu/tOa99r+24ezo15TJTuBVORmXLB2u467L/oMnYnMW1PZdCMvXY8MXqRHWsyUG/1oy9S10o1J5EuLdN/n8zW1dGvUa8EPppapc6+B6txrYNTRM7GUb43+3j7jVmpjKKffsIUbtCLFXpJIDwGqHHb3p7HvtmTStji9bDJlwbrYdxWLopzYTbO/9zzK9xvTb8fpIu5EqskpHxwYv5fcpU+N0f1fLND5T4yK+nqimQ6dGjBrrWtDGrNh1OKKUNf+Ukfvv+KF8brj/Vlx3/PxjHLd8f6spimjf/rKRN3QZ0qaJU1NeO+nWMde5BDmsogGxq4sDnHesL26KS9WIrPLt6l0864Wvf4WrQ/WVU6CD17UR8Wmee0opXfH9LGhLXs9bqyq0f++Ds7GWFVdl9YQzmhyqRrfWFWjkx/4Wn/6YJZGL9moez6bp+tem+zZ+Rt71DUE1LnXwKxNtLC7tqFF7h6n/v3Fgrg3LyI11kPhZoYF02P13ksk1c+lw+/B7bcmBNtdM9IYfpyJb7jTwblzsgsBkGjn6P99vadXbtXu5oGmWKebzXFmgc2GV0a3rAcih14nG7xJlKIknsufG6efvpL9GQ0jR6nE4pfrqf+G5SGNNVlVNJtTzBs8e/U2jc7RCW5yCQGqHBCrEqioyr2x2dGq9nRnqEhUSQ4MBT+sbd6jI5nKtbquwfVuupHcHtbR2KhLlLPlvMdHOVpewKaeoHh7WNfxaN3sF8YJQCK6yl11Ue8WNV5AbAw19lb66O5PtAuNX7zWvLv6pjiN1EwPf7y29yT99u0ZKe/nO2vqtd5hzowRiza0CAxlo8EXHtBvXF86NfCIhfHzFf7z03n624ezY85e5ZVsNa7Dj7/wZLjpXMz6VeWuOk8CGMkIBKwaAlart+xS17uDw66eCcsLU1sf0OvjVjbNPOWmX/aZojMeSa4HemNQKd6w3WiiJU3/a/89w5rCbx7EaoJFO0R+2296UuUI56SeycRhmUwTc3dtgzZsdx4ocWs4lFd9KZ0M21y5MX4bItr5cvWW2PtrrDZ/orQAiWysqtHgNG52ptNzyO8jNPzupjem6HtP7kll8OG01c3y5GWiPk7VFS+M1y+j3PRdtXmnb2dczEUEqJB1lbvqtDisp87GKNMjJ3OIJwrshCe3DL+DGN5bKNHJ5ea+U5OaFrlZ+Ry+b0iUBmU6Gr/TsoodrlyMlW3ZpQcjppMeODf5pKlXvZT9O0L56OKnRuucx4KJya21em740rjdm70OaMc6b29Nokfmk18vcak00SXTPTya+iSmCr+57zTHwaxkxfqtrW1e0zVOMJHJRlXksBe/NOSz0Y7ctKPGkzvgXjnxP1/plCTy1Hnh/16frCP/OShmz5FXRi/XA18u0HtT3Z9VbUoSs5ctWBu8aeMkx1EqqsNyRiZzTCbzHcI5Od469xqoqS7M8La7tkGdew3Uq2OCPWIa8wj1Hb8y7oyxknTda5OaUgWE39BsnKUtnmeGL9WoxRV5lzfzfxHn3cjz5DSHQyEbLY8R8FqRIBCWyPWvT9Zt78xwLal1fUPAeU44pGXk4o3N8pT9/aM5+k1YMDydWQljcasJsLOmXp17DdR5j49Sl38MijuZEJwjQJXH/BrI/fGL43Xp02NcW17U2Eu0BPG2+YkxmWEnE5Zv1tIkkzw2rdfh+9zoYeDWNp+1elvUhIpjlzbP4ZDMncZ4/NJVOJdUhAV2y7bs0v++XqJfvzUt5vuTSZaabeHDhuM1Uicsd2v2Gn94PEFy8sjhQBurahwlbY1Vlzw6ZFGzXHaNh10mUyGlOstVPH48tUUG+W57e3qzGzEt3p/pAiGqxjokEGOnb8x7k2jq90xrDMbHOjVGu7GXquo0JjhxmxtDuhsTmvcJzUL4aihh/Pbqer0wYlnMz0nSzLJtTX8/8OUCB20TqyGhnh6L1lfpxjeman0K+b380gRyUi8trWher6Xa4zieVIZ1Nt50djqxSqLhtkOizW4bZdG7auv1wsj0ZuttFK0HeabOFbNWpxf0iddLToqeDibaTN1O0ypISji0zpjg9ujca6B+0y92ezhSrPNB5AyK0ayOyGU1cnH8XuVwhgBVDsjW+PtsrcfthHNFUVoQ0b5Jsl3kC0nk9NFXvjA+SkLFzPF7jgm/azy3Vtc1tJh5yYnGu/XhwrfIyk070+5dZEzsO/XhgZr6Ak0cHa2nTeRwoO4PDdMVzyfuyTl2afRGXGSOjcaqMzzvl9t3/yPvZqd6rIfvFdtTSLqbbdGGWIWfqlLNRQR3RCZy9kq8KeMve3qMq8HjWG2vyt3Rh2Y6ydESmUPQ77anMRtyNDtq3Pn+hXnWc+acxxIPi00lKHBBRE7UyHOT0x7RD6cwU3ksmbihE0u6qTUSTUoULR9stJs2v317huN1PpcgwGztnjQXQ+fHL194cv/wLR3eqz/RZBIV26sTBuqQGgJUyKpkeviED+eJd0nj99432Sxeqhf3fwvLTZGoC3wmLN6QWnL2QuIk/1JFVY16j0k+4W9kYvLIffahQQubhhOmys07/smw1mpJhvev1Vt2xf1+TiYBcDpbj5OenOOXOetpFi2473aer0wkuX9nclnay/DmvLFnpdV1Ad+fu7zw4bTV6txrYMYDeM3qPA+3Q7wp41OdtCSaRDOpRRua+cG0xMMcU5/2Pb6b3piasC468p+DMrLuZPSbWOp1EXJS514D9YLDILGTAEDkzN/hnvxqcYvZdiX3eg6GBzQyUaczZMy5P4XNHBzPzpp6XRuj7t0YJw9q514Dmz3u8fDwuCMXkDoCVB770EEDIJq55ZVKYRbzlFhr9cjghZq/tlLjlzmbpjcZM8K6VIev89nhS1u+OQoa+Xt8vSD6HYNkZpxonJFGCnZxh/uWbqhKOu9PvAuMxmXFDQZ4cIs2/Nj8/jNjM9pTrr4hEHWowWtjV+qSp8Y0yz8XS+RPVLmrThMS1HnWSuc8NlLdHxoW8z2/e8fZHUK/Dsv2g7k+yOuS7v5biOeqquo6nfHIcMezK30amvUq3Xw0yaiqro869ATusUo+DvhaaGheLOEzpS6r2OFoOI7bhiWYJCIWN6p6r2a9izxPpTqz9/MJesM4Ud8Q0Mujl6smvDdMRPmeHbFMP3lxQtLL9jJfYnj78JUUbjoivioXelKOW+r+9TD2IEDlsaeHOQvCNLLW6vvPjNX/e35cyskqk7W7rkGvjF6hq1+a2CLhrROJGuUTlrc8yN1Mku5IBs9Die5cZmO9M6MEAWOpDbvIf31cRAOxAC+w3DZycYUufmqMPpmR3PTDfcaVJvX+ZAJg0Xor5Fqs5JKnxzTNyhVuVvk2SfHvsEZq3M1veXOqrnttclPep5v7Tm2akj4fhqUWFWX/O6Ta6H80i0OOU+HkPJTNX3v0ko0asSj+EIdsmLV6m9ZVVuupDE9w4ES8OrGq2l/DRlNpa/nZZzOTO99Je4KV4Spi/C7fe3K0znw0OBQsmRrGix7jbnEa9E1GKhfuCfMiplDxRbsuiOb9qav16OBFemmUOzmgwvUNu1nrVLqBj8bTyNBo+a+QtOdHRL/GXrAu/Rte3MDPLAJUOSC83VtTH0h73HAuSObuabRzn58urqd5NJ34G+NLPVkv4lu2IThEK9njePGG2O+viDK8bHjknd04jcR/f9H8RGtM9GSW6crkHUk3e1yMCeVwasyX0Dh0dsSiCj04MHa+idmhYFgmfBWlwbqsItgTr7quQb3HLG/Wo8CJ3A+x+Vui4Y1u3MWN5Zd9pujmvgw9CDc1ydnGvOSHHoNuitW7O1nRhhTGClo5qd8ykT8mlfPAshQn4QmXqeGWmRIZ0w9vHzi9sduYAy2V3JuJzImyHVdtjp+L060ZBNduy68AdapitWkCAesoOf+bE6MHGUcsat4+XhN2AzOTsxrDOQJUOSDarA5OJJt3JdENYHcuLt0/8KPlUfFVhCqPJHsBjMyITDT5VpQ8GOlMRrDTpcSvfpodSgomAB+eILGnpKQu7MMbSTf0mZJSuRqFV2WRuQ7+GpYnrtH8tdtVtmWXnh2+VA8PWqSPZzifmTRyfeFuf3eGeo9JfEc6lca4mz3P/N6QjMxNEdnLKjJJbz5ZsHZ73ATg6aqua9AHU8uS2gec9Jbx+z7lN447sGcwGn77u86TLKezfZ2mnUiUyDmaZGaVjiWVYWy57pHB7iUoj+R0Vwm/eUTt4a5nYhxzd348J2qP+VRd27vlJDWJxLomnphns017hQBVDgifFjyZc6tbF5mpXlCs2brLUbdbJzMyxRs+UYh5PbyS6O5RoahvCLSYNebJr/bMRHffgHkxp5puCDuI7/88tS7CkUnNYwm/CxlrGt1ofv5q7MS9yXg0g43HVFz+3Djd8uY0R4nAnV7IRMuhlwlVMYJB5z0+SqWbg8FIJ8nYw0UN7ksaOGedHh6UeEjd/3tuXFLry2eFHtiI7HH5g2fHxk0Anq5HBy/SXR/PVZd/DNLSDEyCQLsidyQ7k97IRanljXry6yWOem1ky6NDmtfRC7I0usJPVZ2b90xTPebrImb7S6athZbCj7H5MXqSNgZ0ncze6KRn4SYHM5Y69aELwWYQoMo5XibtS1ZNfUD/HZL4IifdXhaxLrLgXPh+FW26aTT3wsjluumNqRoVOjmu2LhDz4bdNX1z4ipd91rLi7P6hkBTLp2q6nrtDp04P5yW+IQWHih2OmvIr9/c877XIvOJZUHkSd8veZvu+Wxe1OfDSxevEe63vCWD5gbv4I5a7HwyBDesTKGXXi6dwyKlW3Z/7P2Z8Z8v52d1feEzLQ2e5798LeOWbmrRC7KgeXDYR1vl1l11uqnv1JSX+f7U1CY2iuRGXZDNiQQyzVrbLNCTqWZ9pm8kGEm9x5LUPNzOJGfyjewtHi/ANHrxxhbHUmSHhieTzH/oSl5jpI0AFZrEyq2yfGNwbHyq9frWnXt6cUSb/nz11vR65fg9B1UuCG8YtEiMjhZWbQk2DBsDMOOizPS2YuPOFsNs3wtr3DqZwtupWIGfeWE9hcrTPM5SEVmqTAUn4gZVk1iljfF3pLnllRlp6Gb7zvS7k8uyu0JJu1zq2Sv5MzARTz63e9/xYF9yU+RFSbrH4sAkezMitnjHTbIpMCJ7+iU6Ju94f1azx+s8mCkwX4X/9Nt3Zy4fX7hXMxw8snInn1gucqtN9Pnstc0eT14Zf0KwyLWWR4wwiFZHxLtZWrbZWeB3yYbC3M7ZQoAqx3jRtfbyNIdxhM82GG0Iyvy1aXZLjpaCyk99kB0Y5aCbKnLPmojG7I4MJkWOJhB2HHhxSEQ2/kekOCV3IrEahOsrq6NeKDr5LeLVIQ0Bq/u/yG6PkXRF6wofLb9DeMOtOgM9xe78eI5ry0r73JEkv/QA9KNM1C87a+rVb2Kpa+dzJ2kPuHueOZnIYvrnD2bFPCozsSVzq2WZGbn8G3wQpwdcsvvL2KUbNXCO24Ho3K1/Zq7e5nURJCntoeWlpDLxBQJUOcbrE4Mf2275cNHwx/dmel0EJMODA9Fp3qlwmU6F8PjQxEN4w23dld2p3P81IPpQPifCf7rnIgI5H0xbnXN3z256I/lhLVe/lHzi0Hw2eF7si5HPZq2N+VqjeOdPL++pzPPpjHEPDlyoewfM16gl7gxdzeS04Ks279RlT4/R5iR69OTajbRoMt3+qtxdp7FLW/ZQbrQ9yZs+fhli7I9SZIeTPcQv2yUZ178+pUWC/ty/GkndnCwFqFZsdN72ilXFRu5vubj/5TsCVDnGywZNtDVf//rk5JaRpeLnWlWTbCML/pKJ/Xremkq9PyXxsJl4dUL4cIZMHBMvjIw/CULOB49DxX91bPNhr5/MWONBYZyZneW7mLlW16bjyzTvlvv1eEi1l/RX89frP1/ED/okqhtfG7tCnXsN1IcRQ56tlbaGhu6OcSlA5SS/YqptrN5jVmjR+ioNijHsNHLmVan5BDiILvJ3i0xSXrmrNmvJwVOVB3FIOOTWps7FXWbA7MQ3aVIRmXR+VbI3a6P8mJHnYo5R/yFAlWP8dgzFu7PlpmSb9clWNn77XZEdZz4yXDf0mSJJ6jt+pfMeN053yDR2rMufG6den8zVnR/N1vbq2D2PfvbKpJzJd5KpHpixlpv2+rJcMTgt7+vjYufRuOKF8S6VBm5bHufOrx97Jydya7/p6jN+pRridNVcmyBnT2Puy79/FBz62TgseeKKPW2LN8aXplXOCcuDCct3O5jNaXhoGHK62yPy85c+PabFe+b6tOdaprixi0cGrBINx6l3eda9VC5kF0YJoOXg4Z6yVE6jmQrm09bPnJkpzGi8OkGwyRi1mNQgvDf45hQndcrF822hIUCVY4jyOkN3TUjBxumOmti909ZWVjfdnb//iwWO72gvr2icOMCqtj4Q92RXU9+gV0YvT3l66v7TyvXSqNg9lcJzvBWqdOrFdZW7tTO0j4T3nqhrCKgqzr7jpTnlhXVhmwle9GZyMt11rugf1uPp6WGxZ0nq9cncqM/HujDZWBUcIvfCyOVJncdfHbNC33tydNTXPncy/DL0/4oUZqZMVSG152obAjrh/q+ysq7wevyJr5KbwSuRVC5sa10OknnNrZEcGcv3FqN4VXFu9MEb/dOcLOiL2WuZeTxPEaDKNV42aAqoMYX88Of+s3X8fUOjvnbBE6NSXu7sUIDg4UELddQ9g1XfEPvgeG3sSj0yeJHenrSq4O/aZOqC7OXR8YcaxnPGIyP0o+dbDnH6U8TsTely8tUL6YK1EOVTAu47P9qT7P6j6eVJf35G2dakPxMr94iR9NCghZ7OntV46N772Tz1m1iqXSkEt1O9ieEpH+7SK0NBRmOMb/Or5XJVn6tlt/Ll7tpM4ynC7+XMFZt21DjaX+sznbAVSSNA5TvxDxL3egZlYop0DnD4yxdxxsSvdOFOeWPS7//ESL5rZZt65+yqbfA0AOHF8TlkfvR8LG4bsSj67ICJesn0m1gqSVq+cae27Wp+F+6rBRtcKVujPTM45lc9masNaS962SZ9p9fDCmPB2u1atL750KQlG6o0YXnLYf3rKqtTWkdkj7KfvjxRyzfuqZdHR+SeuvB/e3pIuT97Vup1ZFVNnaaF9WS9d8B8LY0TLOvca6D+9P5M1QeaB6SufjmVSQnyqz5xLvb3vv714LD99dtb7pdD5zev1/MoZpw1KzYm2XbyMnduxLozXZK0swoU0uHswZc1Co5qgP8RoPINZ9VaMsezzcCJN+oFXxJn+ExcFERb5uot8fNeRIpMwofCkihPSjoaD4909rF8abRk+2Ig0fruHTC/6e+NVTUZ7eHy2riVid+E3JZg/3nd6T6Q4n747uQyVwLvkvSDZ8fqsqfHNnvukqfG6LpXk5sYpdG4ZcE8UOF6fTyn2ePI4crVddntUeR4+0R4cODCpGf1/GzW2hY5qJKb4KAwIys2ie/dELCutzmnrmRIvdPf1Mk1SHVdg+oaAllp4yQsjgvnfwKezS3d0HJyiGTqrpp6d84B4fviY0MWu7JMZBYBqhzj7Qi/9Nee6kko6VkbksTFY2G75zOHydFTEB7UpfHiD6lUQ05mVIxnZ22O3LUrkH3UbzPqzV+T/kxk//x0btThqpJUubsuat6n3R7tl3/+YFZWkoRbazXVYZ6+VIYqStLKZHuU5JMMN0r9ds5cvdWdtmiqvQ7ziZF09L1DdPVLE1q8lst5ZNPZZf22v6fr4qdaTg7htXWVu3129kc0BKhyjJfD6Ori5NnJtGeHL435mt8uNJB7lkS9y+Ou3G1uuSeXe4K9PTnzU8Lv8kMQK8ltFG8oE5z739fuJHOuqo6e++jiJ0frnMdGtnj+L/1nubLeZCXbofSFkctSWk//aaubDRtM1u7aBk1asZlhIR5J95wxY9U2R++rrQ+o3KXgkxPxJj7JN4na6LPLK1sEZl4d685N42y07ZC6eDPAZkout0MLCQGqHMNx5S9uDadAfrLWnTtibsz+xUk5PmO8z6NXaNPOI3sqQrPjRZqVYFhZtF5XH6fY0ygdjw9NbVhGurPyXfbMGF3be5L++UnzXrbjlu7JxRWtjndUleRDnezz+4O7HZ47y7bs0tn/bRnARXZEHi8bY9RXjpYVdmCNXLwxzjvdZ21+HNbZ8qJHgVq2kf8RoMoxXl9kptvtNt8qhXRmgoN/ZLJbdfgxO83hUJNIfSeUpl+OtJeQvmx3X/e6vgT8IJ38d49FCQz99cPZ6RSnSTo9m8K5Va9EuxGwanMwQDd55eZmz//f66nl4kJy8m3IE8KEbVu3cg3luh3V9br/8/mOA6u5JtHx/P+eiz5EHYWHAFWOyeVx2ZL3PRRQmKrrGjzLtdLISBq2MPpsc5EiZ5TLF345/KMnI+ZKCP5361vTNH3VVknS57PX6oT7hqq2PqAdNdGH9knSzBi9pN6dXJYXuXDcqFeMkUo3uzfEi8CKN2hj5qZpqzKffD6Tk6DsWUd6n7+hzxT1nVCqN8aXulIev7FWmr822FO8IcqxSi9yNCJAlSMCAas55dv80Q0CyDGnPzxcx/xriNfFcGzSivycKcjLADsXjMgHs8srdfencyVJD3y5QFU19dq2q1Y3950a8zPRLtqnrNyif4aWE8/u2tiBLwQRE8ldyeYwZVv7X6rbyE9NhEAe72iNQaj6BnrNIbYSrwsAZ3qPXaFHBy/yuhhJnsyzU8Hmeq8yZF7l7jpP1hvMR+CP/dMPd5b9NKFBZC+1RAGseS7MsuY2p7+mSXPbW1nNKNua1jKQmo1VNWpdXJSwMT9l5Z6gduWuxPXdT1+Z6Gj9U0u36hhH79xjw3bve2U11XcOd31Pqkf/VIcpy5/Av7MdwPuzaH7yQ9ugosr7egvNjQ3L9ZeqyETsg+et1y/P+Hbay0VmpdWDyhjzDWPMR8aYRcaYhcaYM4wxHYwxXxtjlob+bx/2/n8YY5YZYxYbYy4Ne/5UY8zc0GvPmmz0w8wx89dm/+Io2pABv1xsA6morQ+4knA8FV7Xan44cr38DbZFXLQPnre+2WMjf/xGfnXXR3O8LkJB6v7QMJ364Ndh+2bwr+q6hpiJhMNnnEw3WJRKcH99locNulGvRFvG1p0ZHmpNheO6TLeVC+3ipDSDEwGF/5b3fT4/Y+uJtr5o3puyOu11+OA+ICJ8Pnut10VACtId4veMpCHW2qMlnShpoaRekoZba7tKGh56LGPMsZKulXScpMskvWiMKQ4t5yVJt0rqGvp3WZrlyjte9H6oSzFpIfHFQpaNbZ/6Oi55arSOvteboX7JHcLuH++FflQOX7ih2eN/fJJ4eFNqMv9LW6frSKEuvvPjPYEo2trZ9Yf3Zuq0B4e1OPfWB2yLHgabdjib5er0h4erum7P8mY66Am3aH16F/ne9vZ25/jbXRvQtgTBOdo6mZfsL3x50kmWs1Bf53DU4vw4EwE5Pw8lfovT+swzHOqumF2+zesiSKLuzgUpB6iMMftJOlfS65Jkra211m6TdIWkN0Nve1PSlaG/r5D0vrW2xlq7UtIyST2MMR0l7WetnWiDtfhbYZ+BhzJx/Lo1a084P3QNRm5wMwmuE5wDm/OynZ5ouullFTuoSeCpL2av1aYdNdrq8iQJ26v3BFp+/OKEhO+/5iVnw/9imbhic+I3+UxkO6KmPr2etks2VKX1eQT5LbRTEaPXYjxPfLUkAyXJHb4K0GW4KMYQy4rnns/m0csMjqTTg+oISRslvWGMmWmMec0Ys7ekg6y16yQp9P+3Qu8/VFJ4/8ny0HOHhv6OfB5hvIj2Rgv8RKtYIsf3An63rGJHs8eZCnKGHy9eB6sK8SgdumB94jeF3NpvegZLAmSO28d2Pef0tNU1JP4N8+FXXrttt9dFgM+5WZ84CXZ5eVy51Zas8SgVRTZ52Sb2VdAUUaUToCqRdIqkl6y1J0vaqdBwvhii7Yo2zvMtF2DMrcaYacaYafX1hTWzjJ8j8kPnO78IBPzgyznZG5NesT14x3XTjgznM0ELtEGAlu2HFRt3RH0f9rCSxiyJ3+sS0ooM9IoP5+e2L9KTyo33Lv8YpF3pzCyaIzvUztr8D1AB8aQToCqXVG6tnRx6/JGCAasNoWF7Cv1fEfb+w8I+30nS2tDznaI834K1tre19jRr7WklJUxAmHEOK/KyLA+bikTidvjZh9ODHUT7Tih1/JkFHkyKkA1e9yIDGvl5X4x6Tgsrb2TgNfKrPDNsafPXI95w4f9Gp164AmGtNCnDQxV9vAs6lunvkOnWnZ/rgXzh9k+8fXdqASrDTCgIIQeV/6UcoLLWrpe02hjz3dBTF0laIOlzSb8MPfdLSQNCf38u6VpjTBtjTBcFk6FPCQ0DrDLG9AzN3ndD2GeQIdFm6APgrlTbQltczkEj+aM3UV1DahMvZIsPfiJAA2YG79HFO17emVwW87Vanx9nuSAbN77yob7hOg/Yw3LL3DHyB+cPY8xlxpjFxphlxpioo+mMMecbY2YZY+YbYxLeJUu3G9IfJL1jjGktaYWkmxQMevU3xtwiqUzSNZJkrZ1vjOmvYBCrXtLt1trGPoy3SeorqZ2kwaF/yKB6Bw1YGh7IV5EnRqf7+oTlm3TmkQdmoETNceIGMssPAdtYNlbVqL2aD/PYHTHk46VRyzOy7m27atXg5x8HPpPZc1Wmz4Tjlm7K8Bryj1vVA9cYhYkch/nFGFMs6QVJFys4Km6qMeZza+2CsPd8Q9KLki6z1pYZY74VdWFh0gpQWWtnSTotyksXxXj/Q5IeivL8NEnHp1OWfOdFRR41ORj1CvJAvHtctfWxg7fXvTpZb9zY3fF6mEAAQLKi1Rq7ahtU1CrVJTpvQExcvjluHQj3rNyU2fxNSKzvhFL9n9eFQFISXQ+RADs3jFhcIelgr4uB9PWQtMxau0KSjDHvS7pCwQ5Jja6T9Im1tkySrLUVLZYSIZ0cVHCV+xWqyVodzckAuW1VWB61x4Ysivve9durHS+395jM9HLIVYXcM8zkeD2Z26XPEtcujOIvJzLAvrbSeZ2Uj/pNWhXztXS2SKIL4a07vZz4ojCPyFyvRwtJpq5B/Bx/ymYbJ1cDcY3HcDZ7L0bui/Tec9WhklaHPS4PPRfuKEntjTGjjDHTjTE3JFooASrfydZRk3g9ySaR8yrpXCFf9CI18faZ18atTPBZ55ZneIajXBPweYMqO8WjvvIDP2cKadwPY5Vwc8SMoB+HJmJww23vzHBtWdmyITRTajqi7Q/h54lozZuyrd5OEBPkbX2S6WZf7OVTj+YKU+SfbbUiw70W/Xxe8Rsvfim2TkpKjDHTwv7dGvF61AFXkcuQdKqkH0q6VNK9xpij4q405eIiq5ZuYFpowA+48wIgkz6ZsUa/jvP6Zk977qBRdR1TwWeaz+9pFKQPp61O/KYwbm/DRG2wLTtrOTYB99Rba6Olc2pULumwsMedJK2N8p5N1tqdknYaY8ZIOlHSklgLpQdVjliwzt1p552cL3LlOny1L+5iAi0RzEK+yLddedbqbV4XwTWJ6pkNSQxLzjcp77dWGjh33Z6HBEqiyrd6AYmVbvZ3m/vip8bo2eFLPVs/xwQKzFRJXY0xXUKT5l0r6fOI9wyQdI4xpsQYs5ek0yUtjLdQelD5TpZaQRlobXk1HvrOj+Z4sl7krq8Xrk/5swwpRXq40vUDN4aFZZrTmibRqfe+z+enXRb4lbf1SaZTO8RePPVozohRQWVyz/Fqfhrah8558Uuxddxnra03xvxe0lBJxZL6WGvnG2N+G3r9ZWvtQmPMEElzJAUkvWatnRdvuQSokBSaBMgH89Y465E4eslGnXL4N5o/yRkuZfQoA5BNqbZZaOsA8LOdtQxjTBU9Yt1lrR0kaVDEcy9HPH5c0uNOl0mAyjfcv3KzaS7SSgpE3IaI3kuKq07kp1/2maILj/6Wjgt7rsGrW3N5oJAbBZZ6Mv+5FIFN9jAhMW/6Iusm/8+Q5Y/6JNuloB7NHY3XILGqxa276rJXGOVf+yPRjNN+5cUxnO71MLKPHFSIa+VmZiFDYVuxsfkEBfcNYLhMqvKsfQj4AkFz93k1K3Gu4WdCIvkWGIonmzcLdlTXZ21dQLYRoEJSCug8A0RV2xDI+DroEQHAqUK6APQKgZjo+F2QbexySBf1lv8RoEJcHMModF7cSc/XC056euQutpx/0dh2Hz+pM5lPCs2WQO4wMpwrARcQoEJM1toWF+c0FQDnOF4AZB41TSz+zyUFAADCEaBCUmjqAZlHjwgATr03pczrIuQ94lwxcK4CvMGxl7LM9/xEughQIa5kDuEJyzZp1OKKjJUFKBRcDAFA8qqqm88M5nSINlVuarjMQ67I1jHOMQGkjwAV4kqmJ8e4ZZt04xtTM1cYoEBwsQQAyZtdXul1EQAAQBoIUCE5yVw5c5VdsEwWtn227lJxNwypyKv9xtr8+j4oAO6ehOLdrCMtcu6jfktd1n87DzcWx3p+YKZs/yNAhZisUh2ny6m+UFk2fTNezAAINEq/Ccb+m4yaugavi4AImUySTtg28ziFogVPYgvsiEA2EaBCXDQOgNSlenFEDir4ScBaLa3Y4XUxfK+qpt7rIgBZxU0YJFJIzZmsHg6F9MOi4BCgAgDfoeUB/1izdbfXRQAcSbXmzGRPKwAuSjEIxDEO5A4CVAAQTxp3xFK/u8xdafjHboauOcJRi0LDPg8/8ipXVFZjYBx8KSNW6X8EqJAUEssB2cBxBgAAgChoJiKPEaBCTESYAW9w7AGAf1AnA7kou92MSMkGuIMAFeKisgVSR84DAMh9tIWiy/Tvws8OwG3U5/5HgApxRebQ4XobAAD4Uqozp0Y8fmXMimaPi7iiAeAjXI6ljmtZ/yNAhbhSaZJt3FHtejkAr3BZAgCFjfNAdIZfBlmW6j6XjZgEgQ/AHQSoClUG2xQbKglQAQCA7Iq8QHTrgjH1GVnzGz8L4A0OPeQzAlSFykmjzbZsfDj5GA0W5JN0Lky4qAEKB0d7/qIqB/yB2cSD+BWQzwhQIa7UutLSkgOk1I8EuokDOYhTX95i03qDmzyI5OdhpeyuuYEJjPyPAJXPZKZuy88D0eTp9wIA5KLgGdy41vj1wTkuxxrymSptdV0gQ0tOD9fD8DtvgwG5VX8h09gfckWJ1wVAUGYOmZZNF5tEc2bNtt0qighhRjvPRD6V6TsIyXwHIBfRhR0oZJzjsi3RNfSabbuzUxCH/HKGYE9FbMG9I5iXtl1W1xztOiHHYu1wFTVVriFAhZh+8OxYr4sAZF2Pzh2klV6XAumiCzcAZBj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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1[[\"traffic_volume\", \"temp\", \"rain_1h\", \"snow_1h\", \"clouds_all\"]].plot()\n",
    "df1[\"is_anomaly\"].plot(secondary_y=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "timeeval",
   "language": "python",
   "name": "timeeval"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
